Effects of a Structured AI Integration Framework on Faculty Technology Acceptance and Adoption in Higher Education: A Quasi-Experimental Study

DOI

10.2991/978-2-38476-599-7_11How to use a DOI?

Keywords

Artificial Intelligence In Higher Education; Faculty Development; Technology Acceptance; The Technology Acceptance Model 3; The Concerns-Based Adoption Model; The Diffusion Of Innovations; Self-Determination Theory; Quasi-Experimental Design; Propensity Score Matching; Mixed-Effects Modeling; Stages Of Concern; Professional Development Intervention; AI Incorporation; Educational Technology Adoption; Behavioral Intention; Perceived Usefulness; Perceived Ease Of Use; Job Relevance; Subjective Norm; Intervention Fidelity; Faculty Attitudes; Digital Transformation; Higher Education Pedagogy; Faculty Training; Technology Adoption Behavior; Intervention Sustainability

Abstract

We ran a quasi-experimental pre-post study, with a four-week follow-up, to examine the Integration Framework for Artificial Intelligence in Higher Education (IFAI-HE). The program is built around six modules and draws on Technology Acceptance Model 3, Rogers’s Diffusion of Innovations, the Concerns-Based Adoption Model and Self-Determination Theory. Our motivation was straightforward: most prior work on AI uptake in academia has been cross-sectional or correlational, with little controlled evidence. Across three Romanian universities, propensity score matching produced a treated group (n = 60) and a control group (n = 60) balanced on age, years of teaching, discipline and rank. Treated faculty followed an eight-week fully online program covering awareness-building, self-diagnosis, sandbox practice, course redesign, peer-community formation and reflective synthesis. Controls received only standard institutional policy documents and one introductory webinar. Outcomes were measured at three time points using validated TAM3-AI scales, the Stages of Concern Questionnaire and an AI Adoption Behaviour Scale. Mixed-effects models showed group-by-time interactions on every TAM3 dimension, with large effect sizes (partial eta-squared = 0.12 to 0.18). Self-focused concerns dropped clearly (Personal stage, d = -1.56) while impact-focused concerns rose (Collaboration stage, d = + 1.67). Gains held at follow-up, with no sign of decay. In moderation analyses, early-career STEM faculty at research-intensive universities showed the largest changes. We tentatively interpret these data as the first controlled experimental evidence that a multi-theoretical faculty development program can shift AI adoption indicators in higher education, and that the changes appear to persist beyond the immediate post-test.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits any noncommercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if you modified the licensed material. You do not have permission under this license to share adapted material derived from this chapter or parts of it.

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TY  - CONF
AU  - Oana-Adriana Ticleanu
AU  - Nicolae Constantinescu
PY  - 2026
DA  - 2026/07/31
TI  - Effects of a Structured AI Integration Framework on Faculty Technology Acceptance and Adoption in Higher Education: A Quasi-Experimental Study
BT  - Proceedings of the International Conference on Management and Entrepreneurial Leadership for K-12 Education Excellence (LEADK12 2026)
PB  - Atlantis Press
SP  - 165
EP  - 187
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-599-7_11
DO  - 10.2991/978-2-38476-599-7_11
ID  - Ticleanu2026
ER  -